1. Field of the Invention
Embodiments of the present invention relate to global localization methods, media, and apparatuses, and more particularly, to global localization methods, media, and apparatuses for a robot using a sequence of sonar operations.
2. Description of the Related Art
Global position estimation and local position tracking are two problems to be solved in a moving robot. Global position estimation determines the position of a robot using data collected by sensors and correlates the collected data with a priori map or a known map. If the priori map is not available, a timing map may be used, with the timing map including recorded time information of the robot moving about within a region. Once the position of the robot is determined on a map, a local position tracking problem is raised regarding a tracking of the robot along a trajectory to the determined position. Once a global location is known, the robot can navigate a complex environment reliably using the map.
In general, the position of a robot is estimated through a probabilistic approach such as a Kalman filter or Monte-Carlo Localization (MCL).
The MCL is a recursive Baysian filter that recursively estimates a belief distribution of the position of the robot, i.e., a posterior distribution thereof, using sensor data, with an assumed uniform initial belief distribution. However, the MCL is disadvantageous in that it is difficult to perform real-time position determination since the initial processing requires a large amount of computation of a large number of initial samples or particles. Also, when the size of a set of samples is small, it may be difficult to generate samples at a true pose. Accordingly, there are no samples at the true pose. Additionally, the samples may not be represented by a belief distribution for all positions in a plane where a robot can move.